1. Field of the Invention
The present invention relates to a speech coding and decoding system, and more particularly to a high quality speech coding and decoding system which performs compression of speech information signals using a vector quantization technique.
In recent years, in, for example, an intracompany communication system and a digital mobile radio communication system, a vector quantization method for compressing speech information signals while maintaining a speech quality is usually employed. In the vector quantization method, first a reproduced signal is obtained by applying prediction weighting to each signal vector in a codebook, and then an error power between the reproduced signal and an input speech signal is evaluated to determine a number, i.e., index, of the signal vector which provides a minimum error power. A more advanced vector quantization method is now strongly demanded, however, to realize a higher compression of the speech information.
2. Description of the Related Art
A typical well known high quality speech coding method is a code-excited linear prediction (CELP) coding method which uses the aforesaid vector quantization. One conventional CELP coding is known as a sequential optimization CELP coding and the other conventional CELP coding is known as a simultaneous optimization CELP coding. These two typical CELP codings will be explained in detail hereinafter.
As will be explained in more detail later, in the above two typical CELP coding methods, an operation is performed to retrieve (select) the pitch information closest to the currently input speech signal from among the plurality of pitch information stored in the adaptive codebook.
In such pitch retrieval from an adaptive codebook, a convolution is calculated of the impulse response of the perceptual weighting reproducing filter and the pitch prediction residual signal vectors of the adaptive codebook, so if the dimensions of the M number (M=128 to 256) of pitch prediction residual signal vectors of the adaptive codebook is N (usually N=40 to 60) and the order of the perceptual weighting filter is N.sub.P (in the case of an IIR type filter, N.sub.P =10), then the amount of arithmetic operations of the multiplying unit becomes the sum of the amount of arithmetic operations N.times.N.sub.P required for the perceptual weighting filter for the vectors and the amount of arithmetic operations N required for the calculation of the inner product of the vectors.
To determine the optimum pitch vector P, this amount of arithmetic operations is necessary for all of the M number of pitch vectors included in the codebook and therefore there was the problem of a massive amount of arithmetic operations.